This study introduces a novel framework for enhancing domain generalization
in medical imaging, specifically focusing on utilizing unlabelled multi-view
colour fundus photographs. Unlike traditional approaches that rely on
single-view imaging data and face challenges in generalizing across diverse
clinical settings, our method leverages the rich information in the unlabelled
multi-view imaging data to improve model robustness and accuracy. By
incorporating a class balancing method, a test-time adaptation technique and a
multi-view optimization strategy, we address the critical issue of domain shift
that often hampers the performance of machine learning models in real-world
applications. Experiments comparing various state-of-the-art domain
generalization and test-time optimization methodologies show that our approach
consistently outperforms when combined with existing baseline and
state-of-the-art methods. We also show our online method improves all existing
techniques. Our framework demonstrates improvements in domain generalization
capabilities and offers a practical solution for real-world deployment by
facilitating online adaptation to new, unseen datasets. Our code is available
at this https URL .

该研究介绍了一种用于增强医学图像领域普适性的新框架，特别关注于利用未标记的多视角彩色底片照片。与依赖单视图成像数据且在不同临床场景中通用性有挑战的传统方法不同，我们的方法利用未标记的多视角成像数据中的丰富信息来提高模型的稳健性和准确性。通过结合类别平衡方法、测试时间适应技术和多视角优化策略，我们解决了常常影响机器学习模型在实际应用中性能的关键问题 —— 领域偏移。与现有基线方法和最先进方法相结合，实验证明我们的方法在与其它领域普适性和测试时间优化方法的比较中始终表现出色。我们还展示了我们的在线方法改进了所有现有技术。我们的框架改进了领域普适性能力，通过促进对新的未知数据集的在线适应，为实际部署提供了实用解决方案。我们的代码可在此 https URL 中获得。